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Category: Spark

Crime Analysis

Raghavan Madabusi combines Zeppelin, R, and Spark to perform crime analysis:

Apache Zeppelin, a web-based notebook, enables interactive data analytics including Data Ingestion, Data Discovery, and Data Visualization all in one place. Zeppelin interpreter concept allows any language/data-processing-backend to be plugged into Zeppelin. Currently, Zeppelin supports many interpreters such as Spark (Scala, Python, R, SparkSQL), Hive, JDBC, and others. Zeppelin can be configured with existing Spark eco-system and share SparkContext across Scala, Python, and R.

This links to a rather long post on how to set up and use all of these pieces.  I’m more familiar with Jupyter than Zeppelin, but regardless of the notebook you choose, this is a good exercise to become familiar with the process.

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Spark Optimizations

Over at the DZone blog, we learn how to use Distribute By and Cluster By to optimize Spark performance:

Your DataFrame is skewed if most of its rows are located on a small number of partitions, while the majority of the partitions remain empty. You really should avoid such a situation. Why? This makes your application virtually not parallel – most of the time you will be waiting for a single task to finish. Even worse, in some cases you can run out of memory on some executors or cause an excessive spill of data to a disk. All of this can happen if your data is not evenly distributed.

To deal with the skew, you can repartition your data using distribute by. For the expression to partition by, choose something that you know will evenly distribute the data. You can even use the primary key of the DataFrame!

It’s interesting to see how cluster by, distribute by, and sort by can have such different performance consequences.

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Tungsten Engine

Sameer Agarwal, Davies Liu, and Reynold Xin show off major Spark engine improvements:

From the above observation, a natural next step for us was to explore the possibility of automatically generating this handwritten code at runtime, which we are calling “whole-stage code generation.” This idea is inspired by Thomas Neumann’s seminal VLDB 2011 paper onEfficiently Compiling Efficient Query Plans for Modern Hardware. For more details on the paper, Adrian Colyer has coordinated with us to publish a review on The Morning Paper blog today.

The goal is to leverage whole-stage code generation so the engine can achieve the performance of hand-written code, yet provide the functionality of a general purpose engine. Rather than relying on operators for processing data at runtime, these operators together generate code at runtime and collapse each fragment of the query, where possible, into a single function and execute that generated code instead.

The possibility of getting an order of magnitude better performance is certainly enticing.

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Using Python 3.4 With EMR And Spark

Bruno Faria shows how to use Python 3.4 with Spark on Amazon’s ElasticMapReduce:

An EMR 4.6 cluster running Spark 1.6.1 will still use Python 2.7 as the default interpreter. If you want to change this, you will need to set the environment variable: PYSPARK_PYTHON=python34. You can do this when you launch a cluster by using the configurations API and supplying the configuration shown in the snippet below:

I’m more of a SQL and Scala guy, but if you like Python and are on the Python 3 side of the divide, here’s a solution for you.

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Exploring Spark

Adnan Masood has photos of slides from a Spark-related meetup:

Apache Spark is a general purpose cluster computing platform which extends map-reduce to support multiple computation types including but not limited to stream processing and interactive queries. Last week IBM’s Moktar Kandil presented at the Tampa Hadoop and Tampa Data Science Group Joint meetup on the topic of exploring Apache Spark.

Apache Spark for Azure HD-Insight

Following are some of the slides discussed in the meetup. To play with the ALS Recommendation engine notebook, please register at www.datascientistworkbench.com which is a free notebook for Apache Spark platform for educational purposes.

Check out the links.

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Integrating Custom Data Sources Into Spark

Nicolas A Perez builds a custom Spark streaming data source:

We first receive the order ID and the total amount of the order, and then we receive the line items of the order. The first value is the item ID, the second is the order ID, (which matches the order ID value) and then the cost of the item. In this example, we have two orders. The first one has four items and the second one has only one item.

The idea is to hide all of this from our Spark application, so what it receives on the DStream is a complete order defined on a stream as follows:

Check out this practical application of Spark Streaming.

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In-Memory OLTP Using Ignite

Babu Elumalai explains how to use Apache Ignite to build an in-memory OLTP system on top of Amazon’s DynamoDB:

Business users have been content to perform analytics on data collected in Amazon Redshift to spot trends. But recently, they have been asking AWS whether the latency can be reduced for real-time analysis. At the same time, they want to continue using the analytical tools they’re familiar with.

In this situation, we need a system that lets you capture the data stream in real time and use SQL to analyze it in real time.

In the earlier section, you learned how to build the pipeline to Amazon Redshift with Firehose and Lambda functions. The following illustration shows how to use Apache Spark Streaming on EMR to compute time window statistics from DynamoDB Streams. The computed data can be persisted to Amazon S3 and accessed with SparkSQL using Apache Zeppelin.

There are a lot of technologies at play here and it’s worth a perusal, even though I’m going to keep recommending that you use a relational database like SQL Server for OLTP work in all but the most extreme of circumstances.

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Building A Prediction Engine

Richard Williamson explains how to build a prediction engine using technologies such as Spark, Kudu, Impala, and Kafka:

We’ll aim to predict the volume of events for the next 10 minutes using a streaming regression model, and compare those results to a traditional batch prediction method. This prediction could then be used to dynamically scale compute resources, or for other business optimization. I will start out by describing how you would do the prediction through traditional batch processing methods using both Apache Impala (incubating) and Apache Spark, and then finish by showing how to more dynamically predict usage by using Spark Streaming.

Of course, the starting point for any prediction is a freshly updated data feed for the historic volume for which I want to forecast future volume. In this case, I discovered that Meetup.com has a very nice data feed that can be used for demonstration purposes. You can read more about the API here, but all you need to know at this point is that it provides a steady stream of RSVP volume that we can use to predict future RSVP volume.

This is pretty dense, but it is a great look at one potential architecture leveraging Spark and several tools in the Hadoop ecosystem.

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Spark 2.0 Technical Preview

Reynold Xin gives a preview of Apache Spark 2.0:

One thing we are proud of in Spark is creating APIs that are simple, intuitive, and expressive. Spark 2.0 continues this tradition, with focus on two areas: (1) standard SQL support and (2) unifying DataFrame/Dataset API.

On the SQL side, we have significantly expanded the SQL capabilities of Spark, with the introduction of a new ANSI SQL parser and support for subqueries. Spark 2.0 can run all the 99 TPC-DS queries, which require many of the SQL:2003 features. Because SQL has been one of the primary interfaces Spark applications use, this extended SQL capabilities drastically reduce the porting effort of legacy applications over to Spark.

There’s some great stuff coming out of DataBricks.  Spark 2.0 looks to be an exciting product.

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Spark Accumulators

Prithviraj Bose explains accumulators in Spark:

However, the logs can be corrupted. For example, the second line is a blank line, the fourth line reports some network issues and finally the last line shows a sales value of zero (which cannot happen!).

We can use accumulators to analyse the transaction log to find out the number of blank logs (blank lines), number of times the network failed, any product that does not have a category or even number of times zero sales were recorded. The full sample log can be found here.
Accumulators are applicable to any operation which are,
1. Commutative -> f(x, y) = f(y, x), and
2. Associative -> f(f(x, y), z) = f(f(x, z), y) = f(f(y, z), x)
For example, sum and max functions satisfy the above conditions whereas average does not.

Accumulators are an important way of measuring just how messy your semi-structured data is.

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